Member SpotlightsA Steering Committee Member Discusses the Field of Bioinformatics Yuriy Gusev, Ph.D. A Science Advisory Board Member Since 2002  Yuriy Gusev, Ph.D., a 2008 Steering Committee Member Yuriy Gusev, Ph.D., is an Assistant Professor of Bioinformatics at the University of Oklahoma Health Science Center, and an Adjunct Assistant Professor at the Breast Health Institute. He is also an associate member of the University of Oklahoma Cancer Institute where he is involved with the Cancer Biology Program (translational research with animal tumor models) and the Southwest Program for Pancreatic Cancer. Gusev obtained his MS degree in Applied Mathematics with a concentration in Mathematical Biology at St. Petersburg State University, Russia and his Ph.D. in Applied Mathematics and Biology from the Central Research Institute of Roentgenology & Radiology, St. Petersburg, Russia. Dr. Gusev was a post-doctoral fellow at the Waksman Institute, Rutgers University in the cancer genetics laboratory of Dr. David Axelrod, and later held a junior faculty position at Johns Hopkins School of Medicine. At Johns Hopkins, he conducted multi-disciplinary projects on statistical modeling and computer simulation of the basic mechanisms of cancer, and the molecular diagnostics of breast, pancreas, prostate and liver malignancies. Academic & Professional Background I received my master's degree in applied mathematics from the University of St. Petersburg in Russia (a more than 250 years old institution with a rich history of mathematical research and education). From early on, even during my undergraduate studies I was fascinated with biology and selected to specialize in mathematical modeling of biological systems. I continued my research in this direction during my graduate studies with Dr. Andrei Yakovlev at the Institute of Radiology and Reontgenology at St. Petersburg, Russia -- one of the oldest European research centers devoted to investigations of radiation effects on living organisms where I had developed mathematical models of radiation damage and repair in renewable cellular systems of gut epithelium and bone marrow as well as tumor cells. I continued my training in the United States as a postdoctoral fellow at Rutgers University with Dr. David Axelrod in his laboratory of cancer genetics (Waksman Institute), studying and modeling the effects of oncogenes on cell proliferation. I continued my research as a junior faculty member at Johns Hopkins School of Medicine where I developed mathematical models of chromosomal instability in cancer cells, one of the basic mechanisms of carcinogenesis. The types of computational studies I do, rely heavily on continued interactions with experimentalists in order to fine tune theoretical models using data from wet lab experiments. During my career as a scientist I enjoyed many fruitful collaborations with biologists, geneticists and physicians in multidisciplinary projects in the areas of radiation biology, cancer research and immunology. Profesional Associations In addition of being an active member of the Science Advisory Board, I am a member of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS), a regional affiliate of ISCB, and also a co-founder of the Oklahoma Bioinformatics Society. I am a big fan of small regional professional gatherings, and have been actively involved in organizing such meetings for the past 5 years. I found these meetings to be very effective in fostering communications between scientists and promoting opportunities for networking and training for graduate students. Hobbies & Interests I enjoy teaching students, organizing scientific conferences, playing sports: volleyball, basketball, swimming and also tai chi; hiking nature trails, preferably with the digital camera in my hands to capture wild life imagery. I enjoy playing chess. I love botanical gardens, natural history and art museums -- can’t get enough of them, and I'm always trying to find a new one when I travel. I also admire history of the arts, science, religion and philosophy; I love literature, science fiction in particular, and poetry, especially haikus, also classical music and ballet. Basically -- I am a quite the typical Culture Junky. Research Interests My current research interests are centered on analysis and integration of high throughput data obtained with genomics and proteomics technologies as well as global profiling of miRNAs in human cancers and several forms of inflammation using bioinformatics and systems biology methodologies. I conduct independent and collaborative research projects with surgeons, oncologists, biochemists and molecular biologists focused on the search for novel biomarkers and treatments for pancreatic and breast cancer, as well as for inflammation and sepsis. My current role also includes bioinformatics training of medical residents and graduate students and bioinformatics consultations for investigators from the Oklahoma University Health Sciences Center and Cancer Institute. What motivates you to pursue this research? We are witnessing a tremendous transformation of biology as a discipline, as a result of technological advances in the post-genomic era when we are flooded with enormous amounts of quantitative information on DNA, RNA and protein functions, interactions and regulation. Biomedical research is becoming more and more dependant on computational disciplines such as bioinformatics, mathematical modeling and computer simulation and systems biology in order to digest and integrate all of this omics data into coherent theoretical models of the inner workings of cells and organisms. It’s a great challenge for all of us and that's what makes modern biology so exciting. Is your career what you expected it would be? Generally -- yes, but I did not expect to witness this “explosion” of new technologies and new paradigms in biology so quickly. The whole field is in flux right now and it’s really thrilling for any scientist to be a part of such a grand transformation of modern science. What would you like to achieve with your research in the future? My career goal is to continue pursuit of basic biological knowledge of the mechanisms of carcinogenesis with the aid of the array of computational methods and tools, and to make my contributions to progress in cancer research towards continued incremental improvements of diagnostics and treatment of this devastating disease. The following are specific to the field of Bioinformatics: What do you believe is the role of bioinformatics researchers in developing molecular dynamics methodologies in order to create simulations for studying important biological systems? Molecular dynamics is by definition a specialized discipline of molecular modeling and computer simulation based on statistical mechanics. It has been developed over the last several decades and currently it’s playing an important role in determining macromolecular structure-function relationships for protein, protein complexes, DNA and RNA. This is truly a interdisciplinary research area employing a wide range of methodologies starting from theoretical physics and all the way down to the computer simulation algorithms and parallel computing. Bioinformatics expertise plays an important role, especially in such applications as protein structure and protein-protein interaction studies, RNA secondary structure predictions, as well as in related fields of chemo-informatics, x-ray crystallography and protein tomography. How critical is it to develop data mining algorithms in the absence of biological data when supporting basic research as opposed to supporting drug discovery and development? Data mining algorithms are critically important in our quest for integrating high throughput omics data and finding molecular correlates for phenotypic changes in cancer and in other diseases. So far bioinformatics has utilized many approaches that were developed in other technical disciplines such as biostatistics, pattern recognition, and even theoretical physics. Theoretical methods have proven to be very practical when applied to real biological problems in basic biology or in drug discovery. Support vector machines (SVM) could be just one example of the success of such methodologies. What other tools -- that don't exist today -- would help bioinformatics researchers conduct their data mining operations? Systems biology research of intracellular regulation could benefit from further development of theoretical methods for network analysis, including analysis of topologies of protein interactions, and perhaps multidimensional analysis of several types of regulations: transcriptional, post-transcriptional and posttranslational. The theoretical developments in control theory and signal processing fields are very sophisticated but have yet to find the right applications in biology. I also believe that the bioinformatics society would benefit greatly from further development of global open access depositories of all omics data. How much exposure/training should life scientists -- not intending to become bioinformatics researchers -- receive in the discipline during graduate school? It’s not only about traditional bioinformatics methods anymore. Computational biology is becoming an important and integral part of any modern biological research and therefore it is critically important to educate future biologists from as early as the undergraduate years to have practical experience with computer software and methods of data analysis. But it is not only about hands-on experience. Future generations of biologists should be more at ease with mathematical quantitative types of thinking, statistical concepts and systems analysis very much like it happened in physics about 100 years ago. Do you have any future predictions about the role & importance of biostatistics/bioinformatics in the life sciences? A short term prediction is not difficult to make: it is already evident that life sciences will become more and more integrated with information processing, computational sciences and less experimental. The bioinformatics, control theory and modeling will play an increasingly important role in shaping future systems biology and personalized medicine. How are modeling and computer simulation tied to your specific research in cancer cell biology? Quantitative models of all kinds: mathematical models and computer simulations are critical for integration of biological knowledge. Cancer cell biology and studies of basic mechanisms of carcinogenesis traditionally were subject to many theoretical models and computer simulation. With the arrival of high throughput methods, we are now facing new challenges of incorporating all this wealth of information into new, more detailed and more sophisticated models. To discuss Bioinformatics and other topics with fellow Science Advisory Board members, please visit our community forum. Web Links Faculty Page Under Construction Recommended by Gusev: Bioinformatics World Wide Publications 1. Y. Gusev Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA. Methods, 2008, 44: 61–72, featured in the “Science spotlight” section of the Ingenuity website (March of 2008). 2. J. Jiang, Y. Gusev, I. Andrea, , T. A. Mettler, D. M.Nagorney, D. Brackett, L. R. Roberts and T. D. Schmittgen. microRNA expression in hepatocellular carcinomas associated with hepatitis infection, cirrhosis and patient’s survival. Journal of Clinical Cancer Research, 2008; 14 (2). Featured on the cover of the January 15, 2008 issue. 3. Hanas JS, Hocker JR, Cheung JY, Larabee JL, Lerner MR, Lightfoot SA, Morgan DL, Denson KD, Prejeant KC, Gusev Y, Smith BJ, Hanas RJ, Postier RG, Brackett DJ. Biomarker identification in human pancreatic cancer sera. Pancreas, 2008, 36(1): 61-9. 4. Y.Gusev, T. D. Schmittgen , M. Lerner, , R. G. Postier, D. Brackett. Computational analysis of biological functions and pathways collectively targeted by co-expressed microRNAs in cancer. BMC Bioinformatics, 2007, 8(Suppl 7): S16, , Featured in the “Science spotlight” section of the Ingenuity website (March of 2008). 5. Y. Gusev and D. Brackett. microRNA profiling in Cancer from Bioinformatics prospective. Expert Review of Molecular Diagnostics, 2007 7(6): 787-792. 6. Eun Joo Lee, Myungwon Baek, Yuriy Gusev, Daniel J. Brackett, Gerard J. Nuovo, and Thomas D. Schmittgen. Classification of microRNA processing patterns in tissues, cell lines and tumors. RNA 2007, Nov 19; [Epub ahead of print]. 7. J. D. Wren, Y. Gusev, A. Ptitsyn, S. Winters-Hilt . Proceedings of the 4th Annual Conference of the MidSouth Computational Biology and Bioinformatics Society. Editorial. BMC Bioinformatics 2007, 8(Suppl 7): S1. 8. E.J. Lee, Y,Gusev, J. Jiang, W. L. Frankel, D. Morgan, R. G. Postier, D. J. Brackett and T. D. Schmittgen. Expression profiling identifies distinct microRNA signature in pancreatic cancer. Int J Cancer. 2007; 120:1046-1054, Published Online: 5 Dec 2006 DOI: 10.1002/ijc.22460. Featured in: T.Hampton. MicroRNAs linked to pancreatic cancer. JAMA. 2007 Mar 7;297(9):937; Also featured in : GS Mack. MicroRNA gets down to business. Nature Biotechnology, 2007 25( 6): 631-638. 9. J. D. Wren, Y. Gusev, A. Ptitsyn, S. Winters-Hilt . Proceedings of the 3rd Annual Conference of the MidSouth Computational Biology and Bioinformatics Society. Editorial. BMC Bioinformatics 2006, 7(Suppl 2):S1. 10. J.Jiang, E. J. Lee, Y. Gusev and T. D. Schmittgen, 2005 Real-time expression profiling of microRNA precursors in human cancer cell lines. Nucl. Acids Res. 2005 33: 5394-5403. 11. Y. Gusev and P. Armitage 2005 Bacterial growth, division and mutations. Chapter, in: Encyclopedia of Biostatistics, Second Edition, P.Armitage ed., Johns Willey & Sons Ltd, NewYork, vol.1: 243-248, 2005. 12. Y. Gusev, J. Sparkowski, A. Raghunathan, H. Ferguson Jr., J. Montano, N. Bogdan, B. Schweitzer, S. Wiltshire, S. F. Kingsmore, W. Maltzman, and V. Wheeler. Rolling Circle Amplification: A New Approach to Increase Sensitivity for Immunohistochemistry and Flow Cytometry. Am J Pathol 2001 159: 63-69. 13. Y. Gusev, V. Kagansky, W. Dooley 2001 Long Term Dynamics of chromosomal instability in cancer cells. Mathematical and Computer Modelling, Vol. 33, Issue 12/13:.1253-1273. 14. Y. Gusev, V. Kagansky, W. Dooley 2000 A stochastic model of chromosome segregation errors with reference to cancer cells. Mathematical and Computer Modelling, Volume 32, Issues 1-2, July 2000: 97-111. ### << Previous Next >> [ View All Member Spotlights ] |
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